Systematic review and meta‐analysis of urinary incontinence prevalence and population estimates
Bibliographic record
Abstract
INTRODUCTION: Incontinence impacts the quality-of-life of people suffering from the disease. However, there is limited information on the prevalence of incontinence due to the stigma, lack of awareness, and underdiagnosis. OBJECTIVE: This study aims to conduct a systematic review and meta-analysis of overactive bladder (OAB) and nonobstructive urinary retention (NOUR). METHODS: The authors conducted a systematic review following the PRISMA guidelines using Embase, MEDLINE, and PubMed databases to identify the relevant publications in the English language. Two reviewers independently assessed the articles and extracted the data. Review papers were assessed for content and references. A meta-analysis of proportions was conducted using the RStudio software. To address the age heterogeneity, a subanalysis was conducted. Pooled data were overlayed on the Canadian population and a sample of 10 populous countries to estimate the number of people suffering from incontinence. RESULTS: Twenty-eight and eight articles were selected for OAB and NOUR, respectively. The pooled prevalence of OAB in men and women was 12% (95% CI: 9%-16%) and 15% (95% CI: 12%-18%), respectively. The estimated prevalence of NOUR was 15.6%-26.1% of men over 60 and 9.3%-20% of women over 60. The subanalysis pooled prevalence of OAB in men and women was 11% (95% CI: 8%-15%) and 12% (95% CI: 9%-16%), respectively. We estimated that 1.4-2.5 million women and 1.3-2.2 million men suffer from OAB in Canada. CONCLUSION: Urinary incontinence is an under-reported and underdiagnosed prevalent condition that requires appropriate treatment to improve a patient's quality-of-life.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.098 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.051 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".